Triple
T35753393
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Coppa Acerbo |
E1033374
|
entity |
| Predicate | 1957F1RaceWinningTeam |
P130014
|
FINISHED |
| Object | Vanwall |
—
|
NE NERFINISHED |
How this triple was built (2 steps)
Every LLM step that produced this triple, in pipeline order — named-entity classification, the disambiguation choices (the exact options shown, with the pick highlighted), and the generated description. The batch + timestamp of each is in the Provenance table below.
NER
Named-entity recognition
gpt-5-mini
Instruction
Given a phrase, classify it is english named entity (e.g., persons, organizations, works of art) in Latin script, or not (e.g., literals, dates, URLs, verbose phrases). For disambiguation, the statement where the phrase occurs as object is also given. Please return a JSON object with `phrase` (string, the phrase being analyzed) and `is_ne` (boolean, indicating whether the phrase is a Named Entity).
Input
Phrase: Vanwall | Statement: [Coppa Acerbo, 1957F1RaceWinningTeam, Vanwall]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: 1957F1RaceWinningTeam Context triple: [Coppa Acerbo, 1957F1RaceWinningTeam, Vanwall]
-
A.
raceWonWithTeam
chosen
Indicates that an individual won a race while competing as a member of a specified team.
-
B.
firstWinningDrivers
Indicates the relationship where certain drivers are the first to achieve a race victory within a given context (such as a series, season, team, or event).
-
C.
chassisManufacturerOfWinningCar
Indicates that a manufacturer built the chassis of the car that won a particular race or competition.
-
D.
championshipWinningCar
Indicates that a car is the specific vehicle that won a particular championship.
-
E.
numberOfF1WorldChampionships
Indicates the number of Formula 1 World Championship titles that an entity has won.
- F. None of above.
Provenance (3 batches)
The batch behind each pipeline step, in order, with when it ran. Timestamps are batch-level — stages were processed in waves, so the object chain (NER → NED1 → NEDg → NED2) reads in order, but predicate / elicitation batches can sit in a different wave.
| Step | Stage | Batch ID | Status | When |
|---|---|---|---|---|
| creating | Elicitation | batch_69f76e1262f48190a313318665acc189 |
completed | May 3, 2026, 3:47 p.m. |
| NER | Named-entity recognition | batch_69f7aa699d68819081ed363931894ab3 |
completed | May 3, 2026, 8:04 p.m. |
| PD | Predicate disambiguation | batch_69f7a8d219f8819081dc4ce3c83ca0cb |
completed | May 3, 2026, 7:58 p.m. |
Created at: May 3, 2026, 4:06 p.m.